{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "![](https://raw.githubusercontent.com/Qinbf/tf-model-zoo/master/README_IMG/01.jpg)\n",
    "AI MOOC： **www.ai-xlab.com**  \n",
    "如果你也是AI爱好者，可以添加我的微信一起交流：**sdxxqbf**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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c7rRTnrQKkX53YMr6Gt1UTJ7fXRFVRSHVtGvFbL3Uow9YXVv3tkVaGuW6jVPce2Qu8wRk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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2139ee7a2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100,)\n"
     ]
    }
   ],
   "source": [
    "# 载入数据\n",
    "data = np.genfromtxt(\"data.csv\", delimiter=\",\")\n",
    "x_data = data[:,0]\n",
    "y_data = data[:,1]\n",
    "plt.scatter(x_data,y_data)\n",
    "plt.show()\n",
    "print(x_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data = data[:,0,np.newaxis]\n",
    "y_data = data[:,1,np.newaxis]\n",
    "# 创建并拟合模型\n",
    "model = LinearRegression()\n",
    "model.fit(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XrV2ANcAPwrLetWb2Xrp3fdQdD9wc/pzZulDQt8HdR8LdsNnAvsB/jnpatq3K\nnpkdAbzi7svHL454auXXRehT7r4PcDhwmpntn3eDctQL7ANc7e57A3+mC8o0jYTHqo4EfpL1eyvo\nEwh3RYeA/YAZZla/NONs4MW82pWhTwFHmtmzwBKCks0VdOe6wN1fDG9fIajB7gu8bGbbA4S3r+TX\nwkytAla5+7Lw/lKC4O/W9QFBB+Bhd385vJ/ZulDQt8jMZpnZjPDnacBBBAeZfgYcGz5tPnB7Pi3M\njrsvcPfZ7r4TwS7pT939i3ThujCz95rZVvWfgUOAx4A7CNYBdMm6AHD3l4AXzGzXcNE84Am6dH2E\nTmCsbAMZrgudMNUiM9uD4MBJD8GG8hZ3/19mtgtBr3Ym8Ahworuvy6+l2TKzAeDr7n5EN66L8DPf\nFt7tBX7k7heb2dbALcAc4Hngr9399ZyamSkz24vgIP1U4GngZMLvDF22PsxsOvACsIu7vxkuy+z/\nhoJeRKTiVLoREak4Bb2ISMUp6EVEKk5BLyJScQp6EZGKU9CLiFScgl5EpOIU9CIiFff/AY7oCER0\neEc0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d4120d9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "plt.plot(x_data, y_data, 'b.')\n",
    "plt.plot(x_data, model.predict(x_data), 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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